|
Twitter
|
Facebook
|
Google+
|
VKontakte
|
LinkedIn
|
Viadeo
|
English
|
Français
|
Español
|
العربية
|
 
International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Monday 23 September 2019

About IJIAS

News

Submission

Downloads

Archives

Custom Search

Contact

Connect with IJIAS

  Now IJIAS is indexed in EBSCO, ResearchGate, ProQuest, Chemical Abstracts Service, Index Copernicus, IET Inspec Direct, Ulrichs Web, Google Scholar, CAS Abstracts, J-Gate, UDL Library, CiteSeerX, WorldCat, Scirus, Research Bible and getCited, etc.  
 
 
 

Artificial intelligence approach to reservoir fluid classification


Volume 7, Issue 2, August 2014, Pages 546–556

 Artificial intelligence approach to reservoir fluid classification

Abbas Mohamed Al-Khudafi1 and Kh. A. Abd-El Fattah2

1 Department of Petroleum Engineering, Hadhramout University of Science and Technology, Al-Mukalla, Yemen
2 Department of Petroleum Engineer, Cairo University, Egypt

Original language: English

Received 13 June 2014

Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Fluid classification is a critical factor in decision of reservoir and production problems. Reservoir fluid can be classified into five types according to laboratory and production data as black oil, volatile oil, gas condensate, wet gas and dry gas. In this work a novel application of Neural Networks (ANN) is presented. Based on production and laboratory data neural networks model is developed for automatic classification of reservoir FLUID. More than 450 samples of five types of reservoir fluids are used to develop the neural network model. About 70 % of data are accepted for neural network training, 15 % for validation and 15 % are used as test set. The importance of different input fluid properties in classification was studied.
The different types of architectures for different groups of input data were tested to select the optimal neural network architecture by fitness criteria. The optimized neural network model was capable of classifying the reservoir fluids with high accuracy. The performance of ANNs models was determined by classification quality index and network error.
The model has been applied successfully to classification of Yemeni fluids using different range of parameters. The results show that the proposed novel ANN model can achieve high accuracy.


Author Keywords: Classification, reservoir fluid type, artificial neural network, model, Contribution of input.


How to Cite this Article


Abbas Mohamed Al-Khudafi and Kh. A. Abd-El Fattah, “Artificial intelligence approach to reservoir fluid classification,” International Journal of Innovation and Applied Studies, vol. 7, no. 2, pp. 546–556, August 2014.